On the road to exascale, multi-core processors and many-core accelerators/coprocessors are increasingly becoming key-building blocks of many computing platforms including laptops, high performance workstations, clusters, grids, and clouds. On the other hand, plenty of hard problems in a wide range of areas including engineering design, telecommunications, logistics and transportation, biology, energy, etc., are often modeled and tackled using optimization approaches. These approaches include greedy algorithms, exact methods (dynamic programming, Branch-and-X, constraint programming, A*, etc.) and meta-heuristics (evolutionary algorithms, particle swarm, ant or bee colonies, simulated annealing, Tabu search, etc.). In many research works, optimization techniques are used to address high performance computing (HPC) issues including HPC hardware design, compiling, scheduling, auto-tuning, etc. On the other hand, optimization problems become increasingly large and complex, forcing the use of parallel computing for their efficient and effective resolution. The design and implementation of parallel optimization methods raise several issues such as load balancing, data locality and placement, fault tolerance, scalability, thread divergence, etc. 

This workshop seeks to provide an opportunity for the researchers to present their original contributions on the joint use of advanced (discrete or continuous, single or multi-objective, static or dynamic, deterministic or stochastic, hybrid) optimization methods and distributed and/or parallel multi/many-core computing, and any related issues.